2. B. Information Technology (IS) CISB454: Introduction to Knowledge Management Knowledge Codification

3. Learning Objectives At the end of this lesson you should be able to:  discuss what knowledge codification involve  discuss the benefits of codified knowledge  find answers to the pre-KC questions 6-2

4. Learning Objectives  discuss the following tools of knowledge codification: Knowledge Map Decision Table Decision Tree Frames Production Rules Case-based Reasoning 6-3

5. Knowledge Codification What does KC Involve?

6. Knowledge Codification in the KM System Life Cycle 6-5 KNOWLEDGE TRANSFER KNOWLEDGE SHARING TESTING AND DEPLOYMENT KNOWLEDGE CODIFICATION KNOWLEDGE BASE DATABASES Decision Tables, Decision Trees, Frames Maps, Rules Capture Tools Programs, Books, Articles, Experts Intelligence gathering Explicit Knowledge KNOWLEDGE CAPTURE (Creation)

7. 6-6 Knowledge Base Shells, Tables, Tools, Frames Maps, Rules Capture Tools Programs, Books, Articles, Experts Intelligence gathering Explicit Knowledge Logical Testing, User Acceptance, Testing, Training Knowledge Transfer Testing and Deployment Knowledge Codification Knowledge Sharing Knowledge Innovation Databases Knowledge Capture (Creation) Collaborative Tools, Networks, Intranets Web Browser, Web Pages, Distributed Systems Database Insight Knowledge Codification in the KM System Life Cycle

8. What Does Knowledge Codification Involve? Converting “tacit knowledge” into “explicit usable form”  Converting “undocumented” information into “documented” information Representing and organizing knowledge before it is accessed It is making institutional knowledge visible, accessible, and usable for decision making 6-7

9. Knowledge Codification Benefits of Knowledge Codification

10. Instruction/training  promoting training of junior personnel based on captured knowledge of senior employees Prediction  inferring the likely outcome of a given situation and flashing a proper warning or suggestion for corrective action 6-9

11. Benefits of Knowledge Codification Diagnosis  addressing identifiable symptoms of specific causal factors Planning/Scheduling  mapping out an entire course of action before any steps are taken 6-10

12. Pre-KC Questions What organizational goals will the codified knowledge serve? Why is the know- ledge useful? How would one co- dify knowledge? 6-11

13. Knowledge Codification Some Knowledge Codification Tools

14. Some Codification Tools Knowledge Map Decision Table Decision Tree Frames Production Rules Case-based Reasoning 6-13

15. Knowledge Map Visual representation of knowledge, not a repository Identify strengths to exploit and missing knowledge gaps to fill Can be applied in Knowledge Capture 6-14

16. Knowledge Map A straightforward directory that points people to where they can find certain expertise Capture both explicit and tacit knowledge in documents and in experts’ heads 6-15

17. Knowledge Map Relationships among Departments 6-16

18. Knowledge Map The Building Cycle Once we know where knowledge resides,  add instructions on how to get there An intranet is a com- mon medium for publishing know- ledge maps 6-17

19. Knowledge Map The Building Cycle Main criteria  clarity of purpose  ease of use  accuracy  currency of content 6-18

20. Decision Trees Composed of nodes representing goals and links representing decisions or outcomes All nodes except the root node are instances of the primary goal Often a step before actual codification Ability to verify logic graphically in problems involving complex situations that result in a limited number of actions 6-19

21. Decision Treee E.g. Discount Policy 6-20 Discount Policy Customer is Library or Individual Less than 6 copies 6-19 copies 20-49 copies 50 or more copies Discount is NIL Discount is 5% Discount is 10% Discount is 15% Customer is Bookstore Discount is NIL Less than 6 copies 6 or more copies Discount is 25% Discount ? Order size ? Bookstore Not a bookstore

22. Decision Tables More like a spreadsheet  divided into a list of conditions and their respective values and a list of conclusions Conditions are matched against conclu- sions 6-21

23. Decision Tables E.g. Discount Policy 8-Jan-16 22 Condition Stub Condition Entry 123456 If (Conditions) Customer is Bookstore YYNNNN Order size > 6 copies YNNNNN Customer is Librarian/Individual YYYY Order size 50 copies or more YNNN Order size 20-49 copies YN N Order size 6-19 copies YN Then (Actions) Allow 25% discount X Allow 15% discount X Allow 10% discount X Allow 5% discount X Allow no discount XX Action StubAction Entry

24. Frames Represent knowledge about a particular idea in a data structure Handle a combination of declarative and operational knowledge, which make it easier to understand the problem domain 6-23

25. Frames Have  a slot a specific object or an attribute of an entity and  a facet the value of an object or a slot When all the slots are filled with values, the frame is considered instantiated 6-24

26. 6-25 Generic AUTOMOBILE Frame Specialization: VEHICLE Generalization: (STATION-WAGON, COUPE, SEDAN)...... Year: Range: (1940 – 1990) If-Changed: (ERROR: Value cannot be modified)...... Generic COUPE Frame Specialization: AUTOMOBILE Generalization: (SMITH’S AUTOMOBILE, HANSON’S AUTOMOBILE) Doors: 2 SMITH’S AUTOMOBILE Frame Specialization: COUPE...... Year: 1990 Doors: ( ) An Automobile Example

27. Production Rules Tacit knowledge codification in the form of premise-action pairs Rules are conditional statement that specify an action to be taken if a certain condition is true 6-26

28. Production Rules The form is IF - - -, THEN, or IF - - -, THEN - - -, ELSE - - - Example:  IF income is “average” and pay_history is “good”  THEN recommendation is “approve loan” 6-27

29. Case-Based Reasoning (CBR) CBR is reasoning from relevant past cases  similar to humans’ use of past experiences to arrive at conclusions The goal is to bring up the most similar historical cases that match the current case 6-28

30. Case-Based Reasoning (CBR) More time savings than rule-based systems Requires rigorous initial planning of all possible variables 6-29

31. Case-Based Reasoning (CBR) Generic CBR Process 6-30 Partial Description of a New Problem Specify Attributes of Problem Match Attributes in Case Base Case Base Submits Similar Cases User

32. Role of Planning (Earlier Steps) Breaking the KM system into modules Looking at partial solutions Linking partial solutions via rules and procedures to arrive at the final solution Making rules easier to review and under- stand 6-31

33. Role of Planning (Latter Steps) Deciding on the programming language Selecting the right software package Developing user interface and consulta- tion facilities Arranging for the verification and vali- dation of the system 6-32

34. THE END Copyright © 2010 Mohd. Sharifuddin Ahmad, PhD College of Information Technology